Наукові публікації та матеріали кафедри авіоніки та систем управління
Permanent URI for this collectionhttp://er.nau.edu.ua/handle/NAU/58730
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Browsing Наукові публікації та матеріали кафедри авіоніки та систем управління by Subject "004.032.26(045)"
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Item A Comprehensive Framework for Underwater Object Detection Based on Improved YOLOv8(National Aviation University, 2024-03-29) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Savchenko, Mykhailo; Савченко, Михайло ВолодимировичUnderwater object detection poses unique challenges due to issues such as poor visibility, small densely packed objects, and target occlusion. In this paper, we propose a comprehensive framework for underwater object detection based on improved YOLOv8, addressing these challenges and achieving superior performance. Our framework integrates several key enhancements including Contrast Limited Adaptive Histogram Equalization for image preprocessing, a lightweight GhostNetV2 backbone, Coordinate Attention mechanism, and Deformable ConvNets v4 for improved feature representation. Through experimentation on the UTDAC2020 dataset, our model achieves 82.35% precision, 80.98 % recall, and 86.21 % mean average precision at IoU = 0.5. Notably, our framework outperforms the YOLOv8s model by a significant margin, while also being 15.1% smaller in terms of computational complexity. These results underscore the efficiency of our proposed framework for underwater object detection tasks, demonstrating its potential for real-world applications in underwater environments.Item Artificial Ineligence for Synthetic Aperture Radar Image Processing(National Aviation University, 2024-06-24) Sineglazov Victor; Shvidchenko AndriyThe object of this research is the processing of synthetic aperture radar (SAR) images using artificial intelligence. The subject of the study focuses on the utilization of artificial intelligence for the object detection on SAR images. The primary goal of this thesis is to investigate the principles of SAR operation, analyze various systems for detecting anomalous objects in soil, develop an intelligent system for processing SAR images, and evaluate the potential of the developed system for the classification of explosive objects. The research methods include the analysis of existing literature and programming in Python. The findings and materials from this thesis are recommended for use in the analysis of current underground anomaly detection systems, the potential application of artificial intelligence and machine learning in demining processes, and the examination of radar image processing methods. Об’єктом цього дослідження є обробка зображень радара з синтезованою апертурою (SAR) за допомогою штучного інтелекту. Предметом дослідження є використання штучного інтелекту для виявлення об’єктів на зображеннях SAR. Основна мета цієї роботи – дослідити принципи роботи SAR, проаналізувати різні системи для виявлення аномальних об'єктів у ґрунті, розробити інтелектуальну систему для обробки зображень SAR та оцінити потенціал розробленої системи для класифікації вибухонебезпечних об’єктів. Методи дослідження включають аналіз наявної літератури та програмування на Python. Висновки та матеріали цієї роботи рекомендується використовувати для аналізу сучасних систем виявлення аномалій під землею, потенційного застосування штучного інтелекту та машинного навчання у процесах розмінування, а також для дослідження методів обробки радіолокаційних зображень.Item Automated Camouflage Design Based on Artificial Intelligence(National Aviation University, 2024-06-28) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Bashenko, M. O.; Башенко, Микита ОлександровичThe article discusses the development of a camouflage uniform production system for civilian use with an emphasis on survival and hunting. Effective camouflage requires precise reproduction of the colors, textures, and patterns of specific landscapes, increasing hunting success and unnoticed movement. The technological process of fabric dyeing, necessary for the production of high-quality camouflage uniforms, includes fabric preparation, dyeing and strict quality control.. Fabric preparation includes cleaning, soaking, bleaching, and mercerization to ensure uniform dye absorption and durability. Dyeing methods vary by fabric type, with reactive dyes for natural fibers and disperse dyes for synthetics. Quality control includes visual inspections and tests for colorfastness under various conditions. Advanced dyeing techniques such as continuous dyeing, spray dyeing, stencil dyeing and digital printing have been analyzed to offer certain advantages. Machines like the Mimaki TX300P handle various fabric widths with high precision and reliability, enhancing efficiency. Automation using the Mimaki TX300P streamlines the dyeing process, optimizing ink consumption and integrating fabric loading, printing, and cutting systems. A customer relationship management system further automates garment creation, enhancing design, order management, and quality control. Tools like CLO3D enable detailed 3D modeling and accurate pattern reproduction. The customer relationship management system coordinates production stages and provides precise paint usage recommendations, ensuring efficient resource management and high-quality outcomes. In conclusion, developing and automating fabric dyeing processes for camouflage uniforms involve advanced technologies and meticulous quality control, ensuring durable, colorfast camouflage clothing that blends effectively into natural environments for civilian use.Item Hybrid neural network optimization system based on ant algorithms(National Aviation University, 2020-07-06) Sineglazov, Victor; Chumachenko, Olena; Omelchenko, Dmytro; Синєглазов, Віктор Михайлович; Чумаченко, Олена Іллівна; Омельченко, Дмитро ВалерійовичThe ant multi-criteria algorithm for feed forward neural networks training is proposed. It is used two criteria: the error of generalization and complexity. It is represented a review of neural network learning using swarm algorithms. As a result of training it is determined a structure of neural network (a number of layers and neurons in then) and the values of weight coefficients and biases. Modification of well-known algorithms consists in using the concept of Pareto optimality. It is done the research of proposed algorithm on the example of multilayer perceptron for the approximation problem solution.Item Intelligent System of Generation of Camouflage Patterns Based on Artificial Intelligence Technologies(National Aviation University, 2024-06-28) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Nikulin, Dmytro; Нікулін, Дмитро ОлеговичThe work is devoted to the development of an intelligent system for generating camouflage patterns based on artificial intelligence technologies. A generative-competitive network is used as an intellectual element of this system. To solve the problem of the collapse mode, the architecture of progressively growing GANs (ProGAN) is used. The system allows you to generate completely new camouflage patterns for the selected area by iteratively improving the pattern. Due to the mechanism of restrictions, it is possible to fix the desired aspects of the drawing (color scheme, pattern, number of colors) from an existing drawing and adapt it to the desired area. The system provides the possibility of generating micropatterns on the drawings to improve camouflage at close distances. When evaluating a camouflage pattern, the system takes into account additional parameters, such as angle (from the ground and air), time and weather.Item Structural-parametric Synthesis of Capsule Neural Networks(National Aviation University, 2023-12-27) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Kudriev, Denys; Кудрєв, Денис ОлексійовичThis work is dedicated to the structural-parametric synthesis of capsule neural networks. A methodology for structural-parametric synthesis of capsule neural networks has been developed, which includes the following algorithms: determining the most influential parameters of the capsule neural network, a hybrid machine learning algorithm. Using the hybrid algorithm, the optimal structure and values of weight coefficients are determined. The hybrid algorithm consists of a genetic algorithm and a gradient algorithm (Adam). 150 topologies of capsule neural networks were evaluated, with an average evaluation time of one generation taking 10 hours. Chromosomes and weights are stored in the generation folder. The chromosome storage format is JSON, using the jsonpickle library for writing. Also, when forming a new generation, chromosome files from previous generations are used as a "cache". If a chromosome of the same structure exists, the accuracy is assigned immediately to avoid unnecessary training of neural networks. As a result of using the hybrid algorithm, the optimal topology and parameters of the capsule neural network for classification tasks have been found.